metadata

license: apache-2.0
base_model: bert-base-cased
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: bert-finetuned-ner
    results: []

bert-finetuned-ner

This model is a fine-tuned version of bert-base-cased on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.2301
Precision: 0.5948
Recall: 0.6779
F1: 0.6336
Accuracy: 0.9265
Adr Precision: 0.5579
Adr Recall: 0.6812
Adr F1: 0.6134
Disease Precision: 0.2273
Disease Recall: 0.1562
Disease F1: 0.1852
Drug Precision: 0.8136
Drug Recall: 0.8775
Drug F1: 0.8443
Finding Precision: 0.2667
Finding Recall: 0.2759
Finding F1: 0.2712
Symptom Precision: 0.5
Symptom Recall: 0.0435
Symptom F1: 0.08
B-adr Precision: 0.7749
B-adr Recall: 0.8513
B-adr F1: 0.8113
B-disease Precision: 1.0
B-disease Recall: 0.1562
B-disease F1: 0.2703
B-drug Precision: 0.9327
B-drug Recall: 0.9557
B-drug F1: 0.9440
B-finding Precision: 0.5909
B-finding Recall: 0.4483
B-finding F1: 0.5098
B-symptom Precision: 0.5
B-symptom Recall: 0.0435
B-symptom F1: 0.08
I-adr Precision: 0.5725
I-adr Recall: 0.6782
I-adr F1: 0.6209
I-disease Precision: 0.4091
I-disease Recall: 0.3103
I-disease F1: 0.3529
I-drug Precision: 0.8458
I-drug Recall: 0.8873
I-drug F1: 0.8660
I-finding Precision: 0.3529
I-finding Recall: 0.2222
I-finding F1: 0.2727
I-symptom Precision: 0.0
I-symptom Recall: 0.0
I-symptom F1: 0.0
Macro Avg F1: 0.4728
Weighted Avg F1: 0.7278

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy	Adr Precision	Adr Recall	Adr F1	Disease Precision	Disease Recall	Disease F1	Drug Precision	Drug Recall	Drug F1	Finding Precision	Finding Recall	Finding F1	Symptom Precision	Symptom Recall	Symptom F1	B-adr Precision	B-adr Recall	B-adr F1	B-disease Precision	B-disease Recall	B-disease F1	B-drug Precision	B-drug Recall	B-drug F1	B-finding Precision	B-finding Recall	B-finding F1	B-symptom Precision	B-symptom Recall	B-symptom F1	I-adr Precision	I-adr Recall	I-adr F1	I-disease Precision	I-disease Recall	I-disease F1	I-drug Precision	I-drug Recall	I-drug F1	I-finding Precision	I-finding Recall	I-finding F1	Macro Avg F1	Weighted Avg F1
No log	1.0	127	0.2653	0.5472	0.6201	0.5814	0.9128	0.4942	0.6376	0.5568	0.0	0.0	0.0	0.7952	0.8186	0.8068	0.0	0.0	0.0	0.0	0.0	0.0	0.7530	0.7731	0.7629	0.0	0.0	0.0	0.9179	0.8818	0.8995	0.0	0.0	0.0	0.0	0.0	0.0	0.4915	0.6325	0.5532	0.1429	0.0345	0.0556	0.855	0.8382	0.8465	0.3333	0.0370	0.0667	0.3184	0.6587
No log	2.0	254	0.2307	0.5896	0.6632	0.6242	0.9254	0.5546	0.6722	0.6077	0.2222	0.1875	0.2034	0.8093	0.8529	0.8305	0.2083	0.1724	0.1887	0.0	0.0	0.0	0.7663	0.8263	0.7952	1.0	0.1562	0.2703	0.9366	0.9458	0.9412	0.625	0.3448	0.4444	0.0	0.0	0.0	0.5649	0.6600	0.6088	0.2963	0.2759	0.2857	0.8495	0.8578	0.8537	0.3846	0.1852	0.25	0.4449	0.7127
No log	3.0	381	0.2301	0.5948	0.6779	0.6336	0.9265	0.5579	0.6812	0.6134	0.2273	0.1562	0.1852	0.8136	0.8775	0.8443	0.2667	0.2759	0.2712	0.5	0.0435	0.08	0.7749	0.8513	0.8113	1.0	0.1562	0.2703	0.9327	0.9557	0.9440	0.5909	0.4483	0.5098	0.5	0.0435	0.08	0.5725	0.6782	0.6209	0.4091	0.3103	0.3529	0.8458	0.8873	0.8660	0.3529	0.2222	0.2727	0.4728	0.7278

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0